CES 2019: “HTC expanding its Virtual Reality (VR) portfolio with it’s all new VIVE Pro Eye VR headset with eye-tracking technology”

Posted by MarketsandMarkets on Jan 18, 2019 7:41:11 PM

At CES 2019, HTC (Taiwan) launched the eye-tracking technology integrated VIVE Pro Eye Virtual Reality (VR) headset. Eye tracking allows users to navigate within VR apps by just looking at particular icons or places. This was the result of the partnership (signed in October 2018) between Tobii (Sweden) and HTC for developing eye-tracking technology for VR headsets.

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Topics: Semiconductor and Electronics, Eye Tracking Market

“Raven Industries (US), a leading technology company offering innovative solutions to the precision agriculture industry, completed the acquisition of AgSync, Inc. (US)”

Posted by MarketsandMarkets on Jan 8, 2019 6:07:57 PM

On January 2, 2018, Raven Industries (US) completed the acquisition of AgSync, Inc., headquartered in Indiana, US. This acquisition is expected to enhance the Slingshot platform of Raven Industries by delivering a seamless logistics solution for agriculture retailers, custom applicators, and enterprise farms. AgSync, an ag logistics software company, has expertise in precision agriculture logistic management that already compliments the Raven Slingshot solution. With the integration of AgSync’s systems and expertise, Raven Industries will be able to offer an end-to-end logistics solution in the market, improving the bottom-line of its core customers.

Analysis by MarketsandMarkets™:

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Topics: Semiconductor and Electronics, Precision Farming Market, inventory management market

Neuromorphic Computing could play a major role in Artificial Intelligence : A recent study by Applied Brain Research

Posted by MarketsandMarkets on Dec 13, 2018 6:22:35 PM

On 6th December 2018, Applied Brain Research Inc. (Canada), a neuromorphic computing software developing company, released the results of a study that evaluated the performance of their Nengo Deep Learning Toolkit, running an audio keyword spotting deep learning network on Intel's Loihi neuromorphic research chip and compared the energy efficiency to traditional hardware.
 
The benchmarking results show that Nengo DL on Intel Loihi uses 38x less energy per inference than an architecturally identical network running on an NVIDIA Quadro K4000 GPU. The study also compared the dynamic energy cost per inference performance of the same deep network on several other platforms. In each case, the Nengo DL on Loihi network consumed significantly less power. In comparison, the NVIDIA Jetson TX1 edge GPU consumed 7.3x more energy, the Intel Xeon E5-2630 CPU consumed 8.2x more energy, and the Movidius Neural Compute Stick consumed 1.9x more energy.

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Topics: Semiconductor and Electronics, Artificial Intelligence, Neuromorphic Computing

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